对子图像进行拼接以重建原始图像

import cv2
import numpy as np

# 读取子图像
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

# 检测特征点
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# 特征点匹配
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# 筛选好的匹配点
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# 获取匹配点的坐标
src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 2)

# 计算变换矩阵
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

# 拼接图像
h, w = img1.shape[:2]
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)

img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)

result = cv2.warpPerspective(img1, M, (img2.shape[1] + img1.shape[1], img1.shape[0]))
result[0:img2.shape[0], 0:img2.shape[1]] = img2

cv2.imshow('Result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取子图像
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

# 色彩校正
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2LAB)
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2LAB)

# 计算均值和标准差
mean1, std1 = cv2.meanStdDev(img1)
mean2, std2 = cv2.meanStdDev(img2)

# 标准化
img1 = (img1 - mean1) / std1 * std2 + mean2
img2 = cv2.cvtColor(img2, cv2.COLOR_LAB2BGR)

# 图像融合
result = cv2.addWeighted(img1, 0.5, img2, 0.5, 0)

cv2.imshow('Result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()